/// <summary>
/// Initialize the experiment with some optional XML configuration data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
// Determine what function to regress.
string fnIdStr = XmlUtils.GetValueAsString(xmlConfig, "Function");
FunctionId fnId = (FunctionId)Enum.Parse(typeof(FunctionId), fnIdStr);
_fn = FunctionUtils.GetFunction(fnId);
// Read parameter sampling scheme settings.
int sampleResolution = XmlUtils.GetValueAsInt(xmlConfig, "SampleResolution");
double sampleMin = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMin");
double sampleMax = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMax");
_paramSamplingInfo = new ParamSamplingInfo(sampleMin, sampleMax, sampleResolution);
}
private static void ReadEvaluationSchemeConfig(
JsonElement configElem,
out Func <double, double> fn,
out ParamSamplingInfo paramSamplingInfo,
out double gradientMseWeight)
{
// Get the customEvaluationSchemeConfig section.
if (!configElem.TryGetProperty("customEvaluationSchemeConfig", out JsonElement evalSchemeElem))
{
throw new Exception("customEvaluationSchemeConfig not defined.");
}
// Read function ID.
string functionIdStr = JsonReadMandatoryUtils.ReadStringMandatory(evalSchemeElem, "functionId");
FunctionId functionId = (FunctionId)Enum.Parse(typeof(FunctionId), functionIdStr);
fn = FunctionFactory.GetFunction(functionId);
// Read sample interval min and max, and sample resolution.
double sampleIntervalMin = JsonReadMandatoryUtils.ReadDoubleMandatory(evalSchemeElem, "sampleIntervalMin");
double sampleIntervalMax = JsonReadMandatoryUtils.ReadDoubleMandatory(evalSchemeElem, "sampleIntervalMax");
int sampleResolution = JsonReadMandatoryUtils.ReadIntMandatory(evalSchemeElem, "sampleResolution");
paramSamplingInfo = new ParamSamplingInfo(sampleIntervalMin, sampleIntervalMax, sampleResolution);
// Read the weight to apply to the gradientMse readings in the final fitness score.
// 0 means don't use the gradient measurements, 1 means give them equal weight to the y position readings at each x sample point.
gradientMseWeight = JsonReadMandatoryUtils.ReadDoubleMandatory(evalSchemeElem, "gradientMseWeight");
}
/// <summary>
/// Calculate an approximate gradient of a given function, at a number of discrete sample points.
/// </summary>
/// <param name="paramSamplingInfo">Sampling metadata.</param>
/// <param name="yArr">The function output/result at a number of discrete sample points.</param>
/// <param name="gradientArr">An array to store the calculated gradients within.</param>
public static void CalcGradients(
ParamSamplingInfo paramSamplingInfo,
double[] yArr,
double[] gradientArr)
{
// Notes.
// The gradient at a sample point is approximated by taking the gradient of the line between the two
// sample points either side of that point. For the first and last sample points we take the gradient
// of the line between the sample point and its single adjacent sample point (as an alternative we could
// sample an additional point at each end that doesn't get used for the function regression evaluation.
//
// This approach is rather crude, but fast. A better approach might be to do a polynomial regression on
// the sample point and its nearest two adjacent samples, and then take the gradient of the polynomial
// regression at the required point; obviously that would required more computational work to do so may
// not be beneficial in the overall context of an evolutionary algorithm.
//
// Furthermore, the difference between this gradient approximation and the true gradient decreases with
// increases sample density, therefore this is a reasonable approach *if* the sample density is
// sufficiently high.
// Handle the end points as special cases.
// First point.
double[] xArr = paramSamplingInfo.XArr;
gradientArr[0] = CalcGradient(xArr[0], yArr[0], xArr[1], yArr[1]);
// Intermediate points.
int width = Vector <double> .Count;
int i = 1;
for (; i < xArr.Length - width - 1; i += width)
{
// Calc a block of x deltas.
var vecLeft = new Vector <double>(xArr, i - 1);
var vecRight = new Vector <double>(xArr, i + 1);
var xVecDelta = vecRight - vecLeft;
// Calc a block of y deltas.
vecLeft = new Vector <double>(yArr, i - 1);
vecRight = new Vector <double>(yArr, i + 1);
var yVecDelta = vecRight - vecLeft;
// Divide the y's by x's to obtain the gradients.
var gradientVec = yVecDelta / xVecDelta;
gradientVec.CopyTo(gradientArr, i);
}
// Calc gradients for remaining intermediate points (if any).
for (; i < xArr.Length - 1; i++)
{
gradientArr[i] = CalcGradient(xArr[i - 1], yArr[i - 1], xArr[i + 1], yArr[i + 1]);
}
// Last point.
gradientArr[i] = CalcGradient(xArr[i - 1], yArr[i - 1], xArr[i], yArr[i]);
}
/// <summary>
/// Create a System.Windows.Forms derived object for displaying output for a domain (e.g. show best genome's output/performance/behaviour in the domain).
/// </summary>
public AbstractDomainView CreateDomainView()
{
if (1 == InputCount)
{
ParamSamplingInfo paramInfo = _paramSamplingInfo;
return(new FnRegressionView2D(_fn, paramInfo, true, CreateGenomeDecoder()));
}
return(null);
}
private static BlackBoxProbe CreateBlackBoxProbe(
Func <double, double> fn,
ParamSamplingInfo paramSamplingInfo)
{
// Determine the mid output value of the function (over the specified sample points) and a scaling factor
// to apply the to neural network response for it to be able to recreate the function (because the neural net
// output range is [0,1] when using the logistic function as the neuron activation function).
FuncRegressionUtils.CalcFunctionMidAndScale(fn, paramSamplingInfo, out double mid, out double scale);
return(new BlackBoxProbe(paramSamplingInfo, mid, scale));
}
/// <summary>
/// Probe the given function by taking samples of it at a number of discrete sample points.
/// </summary>
/// <param name="fn">The function to probe/sample.</param>
/// <param name="paramSamplingInfo">Sampling metadata.</param>
/// <param name="responseArr">An array to store the sample results within.</param>
public static void Probe(
Func <double, double> fn,
ParamSamplingInfo paramSamplingInfo,
double[] responseArr)
{
Debug.Assert(responseArr.Length == paramSamplingInfo.SampleResolution);
double[] xArr = paramSamplingInfo.XArr;
for (int i = 0; i < xArr.Length; i++)
{
responseArr[i] = fn(xArr[i]);
}
}
public void CalcGradients()
{
const int sampleCount = 100;
ParamSamplingInfo psi = new ParamSamplingInfo(0, 2 * Math.PI, sampleCount);
double[] yArr = new double[sampleCount];
FuncRegressionUtils.Probe((x) => Math.Sin(x), psi, yArr);
// Calc gradients.
double[] gradientArr = new double[sampleCount];
FuncRegressionUtils.CalcGradients(psi, yArr, gradientArr);
// Calc expected gradients (using simple non-vectorized logic).
double[] gradientArrExpected = new double[sampleCount];
CalcGradients_IndependentImpl(psi, yArr, gradientArrExpected);
// Compare results.
Assert.Equal(gradientArrExpected, gradientArr);
}
private static void CalcGradients_IndependentImpl(
ParamSamplingInfo paramSamplingInfo,
double[] yArr,
double[] gradientArr)
{
// Handle the end points as special cases.
// First point.
double[] xArr = paramSamplingInfo.XArr;
gradientArr[0] = CalcGradient(xArr[0], yArr[0], xArr[1], yArr[1]);
// Intermediate points.
int i = 1;
for (; i < xArr.Length - 1; i++)
{
gradientArr[i] = CalcGradient(xArr[i - 1], yArr[i - 1], xArr[i + 1], yArr[i + 1]);
}
// Last point.
gradientArr[i] = CalcGradient(xArr[i - 1], yArr[i - 1], xArr[i], yArr[i]);
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="fn">The target function.</param>
/// <param name="paramSamplingInfo">Sampling (defines the x range and sampling density).</param>
/// <param name="gradientMseWeight">The fitness weighting to assign to the gradient mean squared error (MSE) score.</param>
public GenerativeFnRegressionEvaluationScheme(
Func <double, double> fn,
ParamSamplingInfo paramSamplingInfo,
double gradientMseWeight)
{
_paramSamplingInfo = paramSamplingInfo;
_gradientMseWeight = gradientMseWeight;
// Alloc arrays.
int sampleCount = _paramSamplingInfo.SampleResolution;
_yArrTarget = new double[sampleCount];
_gradientArrTarget = new double[sampleCount];
// Calculate the target responses (the expected/correct responses).
FuncRegressionUtils.Probe(fn, paramSamplingInfo, _yArrTarget);
FuncRegressionUtils.CalcGradients(paramSamplingInfo, _yArrTarget, _gradientArrTarget);
// Create blackbox probe.
_blackBoxProbe = CreateBlackBoxProbe(fn, paramSamplingInfo);
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="paramSamplingInfo">Parameter sampling info.</param>
/// <param name="gradientMseWeight">Fitness weighting to apply to the gradient fitness score.</param>
/// <param name="yArrTarget">Array of target y values (function output values).</param>
/// <param name="gradientArrTarget">Array of target gradient values.</param>
/// <param name="blackBoxProbe">Black box probe. For obtaining the y value response array from an instance of <see cref="IBlackBox{T}"/>.</param>
internal FuncRegressionEvaluator(
ParamSamplingInfo paramSamplingInfo,
double gradientMseWeight,
double[] yArrTarget,
double[] gradientArrTarget,
IBlackBoxProbe blackBoxProbe)
{
_paramSamplingInfo = paramSamplingInfo;
_gradientMseWeight = gradientMseWeight;
_yMseWeight = 1.0 - gradientMseWeight;
_yArrTarget = yArrTarget;
_gradientArrTarget = gradientArrTarget;
// Alloc working arrays for receiving black box outputs.
int sampleCount = _paramSamplingInfo.SampleResolution;
_yArr = new double[sampleCount];
_gradientArr = new double[sampleCount];
_blackBoxProbe = blackBoxProbe;
}
/// <summary>
/// Determine the mid output value of the function (over the specified sample points) and a scaling factor
/// to apply the to neural network response for it to be able to recreate the function (because the neural net
/// output range is [0,1] when using the logistic function as the neuron activation function).
/// </summary>
/// <param name="fn">The function to be sampled.</param>
/// <param name="paramSamplingInfo">Parameter sampling info.</param>
/// <param name="mid">Returns the mid value of the function (halfway between min and max).</param>
/// <param name="scale">Returns the scale of the function.</param>
public static void CalcFunctionMidAndScale(
Func <double, double> fn,
ParamSamplingInfo paramSamplingInfo,
out double mid, out double scale)
{
double[] xArr = paramSamplingInfo.XArr;
double min = fn(xArr[0]);
double max = min;
for (int i = 0; i < xArr.Length; i++)
{
double y = fn(xArr[i]);
min = Math.Min(y, min);
max = Math.Max(y, max);
}
// TODO: explain this (0.8 is logistic function range, 0.5 is the logistic function output value when its input is zero).
double range = max - min;
scale = range / 0.8;
mid = (min + max) / 2.0;
}
/// <summary>
/// Construct a generative function regression evaluator with the provided parameter sampling info and function to regress.
/// </summary>
public GenerativeFnRegressionEvaluator(IFunction fn, ParamSamplingInfo paramSamplingInfo, double gradientMseWeighting)
: base(fn, paramSamplingInfo, gradientMseWeighting, CreateGenerativeBlackBoxProbe(fn, paramSamplingInfo))
{
}
public FuncRegressionUtilsBenchmarks()
{
var psi = new ParamSamplingInfo(0, 2 * Math.PI, __sampleCount);
FuncRegressionUtils.Probe((x) => Math.Sin(x), psi, _yArr);
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="paramSamplingInfo">Parameter sampling info.</param>
/// <param name="offset">Offset to apply to each neural network output response.</param>
/// <param name="scale">Scaling factor to apply to each neural network output response.</param>
public BlackBoxProbe(ParamSamplingInfo paramSamplingInfo, double offset, double scale)
{
_paramSamplingInfo = paramSamplingInfo;
_offset = offset;
_scale = scale;
}
